Aiming at data redundancy and curse of dimensionality in data mining process

in accordance with the theoretical bases and methods of ReliefF algorithm and principal component analysis algorithm

this paper establishes the quadratic feature selection method on the basis of ReliefF optimization and principal component analysis

and gives out the experimental results of this method. This method can effectively process the data with high dimension

redundant and irrelevant features. Combined with machine learning algorithm

it makes the data mining system get an accurate and efficient implementation result

thus providing a solid decision-making foundation for decision makers. The conclusion is that this algorithm has a higher classification accuracy obtained through experiment.